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Presented is a novel ECG heart beat detection algorithm combining Mathematical Morphology operations and Quadratic Spline wavelet transform for hard-wired realization. Experimentally demonstrated in FPGA under the MIT/BIH Arrhythmia database, the algorithm shows 98.16% sensitivity, 99.7% predictivity and 97.29% overall accuracy.
In this paper, an expert experience based Electrocardiogram (ECG) classification method using domain knowledge and morphology information is presented. Firstly, the process of ECG interpretation by physicians is analyzed. Then, the construction method of classification model based on Support Vector Machine (SVM) is discussed and morphology information extraction approach through Principal Component...
There are various kinds of birds in the Qinghai Lake National Nature Reserve. In recent years, avian influenza breaks out in this region for several times. The biologists need to identify the species of birds after the discovery of infected birds. They can classify the birds according to the appearance by experience, or they can identify the birds according to the gene collected from the birds. But...
The main research point is the thinning method to the pavement crack images through using mathematical morphology in this paper. The efficient steps of the thinning algorithm are given for binary images and the satisfactory thinned images are obtained through experiments. In some cases, the thinned images will produce cavities or breakpoints, it needs to be processed by closing method in order to...
The purpose of this study was to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. There are several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy. Considering that during the automatic...
Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics...
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